1. Technical Field
The present disclosure relates to the field of printing and in particular, to systems and methods for the stochastic regression testing of page description language processors.
2. Description of Related Art
Print content, which may include documents with text, image and graphical data, may be represented and stored using a variety of formats. Typically, document processing software running on a computing device may allow users to view, edit, process, and store the documents conveniently. In many systems, when a document is to be printed, the document, or pages in the document, may be sent to the printer in the form of a Page Description Language (“PDL”). PDLs may include PostScript™, Adobe™ PDF, HP™ PCL, Microsoft™ XPS, and variants thereof. A PDL description of a document provides a high-level description of each page in a document. This PDL description is often translated to a series of lower-level printer-specific commands when the document is being printed. The process of transforming page data from a PDL description to lower-level printer-specific commands may be complex and depend on the features and capabilities offered by exemplary printer. The transformation is typically performed by one or more of a language processor, or a raster image processor (“RIP”), which may often be implemented by software and/or firmware, running on a computer or printer. After the translation process for each page has been completed, the document may be printed. The term PDL processor is used generically to refer to software or firmware, such as a language processor and/or a RIP, involved in the processing of PDL input.
Because of the complexity of the process of transforming data in PDLs into a printed image on a paper, rigorous testing of language processing and RIP software is essential. Typically, PDL processors are subjected to behavioral testing where a fixed set of inputs is fed to the software and the output produced in compared with stored known data. Behavioral regressions, or changes in output, manifest themselves as a change from a previously known behavior. Therefore, behavioral regressions can be detected if the input for the regression test was identical to the input that was used to determine the prior known behavior.
A fixed suite of tests has the disadvantage that the same set of points in the input space is tested repeatedly. Thus, potential problems that may be exposed using an alternate set of points in the input space may remain undetected. Stochastic regression testing may be used to remedy this shortcoming. Stochastic regression testing uses random points in the input space to test program code. However, when stochastic regression testing is used with PDL processors, which convert a PDL description to pixels on a printed page, the testing has hitherto been of limited use because the generation of completely random strings of data do not produce syntactically correct PDL input in the overwhelming majority of cases. Therefore, in practice, such tests do no more than verifying the ability of the PDL processor to correctly reject invalid data. The ability to generate syntactically correct random input would greatly enhance the robustness of testing. Therefore, there is a need for systems and methods that would permit robust testing of PDL processors using random and/or pseudo-random techniques.